code: def clean_string(base):
      """
      Tokenization/string cleaning.
      Original from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
      """
      base = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", base)
      base = re.sub(r"\'re", " 're", base)
      base = re.sub(r"\'d", " 'd", base)
      base = re.sub(r"\'ll", " 'll", base)
      base = re.sub(r"\'s", " 's", base)
      base = re.sub(r"\'ve", " 've", base)
      base = re.sub(r"n\'t", " n't", base)
      base = re.sub(r"!", " ! ", base)
      base = re.sub(r",", " , ", base)
      base = re.sub(r"\)", " \) ", base)
      base = re.sub(r"\(", " \( ", base)
      base = re.sub(r"\?", " \? ", base)
      base = re.sub(r"\s{2,}", " ", base)
      return base.strip().lower()
  
file_path: neon/neon/data/text_preprocessing.py
imports: from future import standard_library
  from builtins import map
  import numpy
  import re
  from neon.util.compat import pickle
label_desc: Use image node_id 1 for calling a global function with example usage: clean_string(base) and returns: base